- Model Evaluation
- MLOps (Machine Learning Operations)
- Applied Machine Learning
- Data Preprocessing
- Data Validation
- Debugging
- Feature Engineering
- Data Quality
- Cloud Deployment
- Continuous Monitoring
- Continuous Deployment
- Machine Learning
Machine Learning in Production
Completed by Fabiana Boldrin
June 11, 2021
11 hours (approximately)
Fabiana Boldrin's account is verified. Coursera certifies their successful completion of Machine Learning in Production
What you will learn
Identify key components of the ML project lifecycle, pipeline & select the best deployment & monitoring patterns for different production scenarios.
Optimize model performance and metrics by prioritizing disproportionately important examples that represent key slices of a dataset.
Solve production challenges regarding structured, unstructured, small, and big data, how label consistency is essential, and how you can improve it.
Skills you will gain
